Abstract

Aims: The present study focussed on deriving and validating a ‘genetic bleeding risk score’ (GBRS) based on genetic and non-genetic factors associated with bleeding in patients on long term anticoagulation therapy.

Patients and Methods: Patients on warfarin (n=53) or acenocoumarol (n=257) long-term therapy were genotyped for twenty one SNPs in six genes. Two GBRSs were developed and validated.

Results: The incidence rate was 16.86 and 4.46 per 100 person-years for minor and major bleeding respectively. The novel GBRS (positive predictive value = 83.3%, specificity = 97.4%) comprised of four parameters; age >65 years, F5 rs6025, VKORC1 rs9934438 and CYP2C9 rs1057911.

Conclusions: The present study is the first to devise and validate a genetic based score for predicting bleeding among first time users of oral anticoagulants.

Keywords

Introduction

Natural dicoumarol was first identified in year 1940 in mouldy
hay as a cause of serious hemorrhagic diathesis in cattle [1]. Further
research led to the development of warfarin, a coumarin derivative
that was initially promoted as a rat poison and first showed success
in prophylaxis of deep vein thrombosis in year 1941 [2]. It is now
the most widely used anticoagulant in the treatment and prevention
of thrombosis. Despite its common usage, oral anticoagulant (OAC)
therapy is associated with significant bleeding complications. Several
hospital based studies in India [3,4] and the world [5,6] have ranked
anticoagulant-induced bleeding as the most common cause amongst
5% - 6.9% of hospital admissions that occur due to adverse drug
reactions.

Both, genetic (CYP2C9 and VKORC1 variants) [7-12] and non
genetic factors (drug-drug interactions, additional medical conditions,
age, history of bleeding) [13,14] are known to contribute towards
bleeding or hemorrhage in patients on oral anticoagulant therapy.
Several pharmacogenetic dosing algorithms for warfarin [15-18] and
acenocoumarol [19-21] have been developed so far. A systematic
review and meta analysis [22] aimed to investigate the efficacy of
genotype-guided dosing of warfarin in reducing bleeding events and
over-anticoagulation included three randomised clinical trials [23-25]
that compared pharmacogenetic dosing with a standard dose control
algorithm in patients starting warfarin for the first time. None of the
above studies showed a statistically significant difference in bleeding
rates between the two groups. This is possibly because these studies
attempted to predict bleeding using a pharmacogenetic ‘dosing’
algorithm originally derived by analyzing dosage in patients, rather
than a true bleeding prediction algorithm that, on the other hand
should ideally be derived by analyzing bleeding outcomes in patients. Hence, it would be unreasonable to discount the role of genetic variants
in predicting the risk of bleeding.

Although, a few bleeding risk prediction scores are available
[13,14,26-28], most are derived from the white population and more
importantly, none have evaluated the predictive significance of
genetic risk factors so far. The HEMOR2RHAGES score formulated by
previously recognized bleeding risk factors from the literature includes
CYP2C9 variants as one of the variables in their score index; however its
predictive usefulness in the cohort was neither evaluated nor validated
due to non availability of DNA [28]. The present study focussed on
deriving and validating a ‘Genetic bleeding risk’ (GBR) score based
on genetic and non-genetic factors associated with bleeding (both
minor and major) in patients on long term anticoagulation therapy.
Apart from variants in CYP2C9 and VKORC1 genes, variants in APOE, ABCB1 (MDR1), CYP4F2, F5 and F2 were also analysed in the current
study.

Coumarin derivatives interfere with the recycling of vitamin K in
the liver. Vitamin K is involved in the carboxylation of the precursor
proteins for the coagulation factors II, VII, IX and X. In the presence
of coumarin derivative, the activity of these components is lowered
thereby inhibiting coagulation. The transport of vitamin K to the
liver is dependent on apolipoprotein E (APOE). The prevalence of APOE isoforms (e2, e3, e4; distinguished by two non synonymous
polymorphisms; rs7212 and rs229358) varies by race, and each isoform
has varying ability to facilitate clearance of vitamin-K-rich lipoproteins
from plasma [29]. The e4 allele has been associated with higher warfarin
dose among African Americans and Italians, but not Caucasians
[30,31]. However, none of the studies have analysed the association of APOE isoforms with anticoagulant-induced bleeding.

The intestinal bioavailability of oral anticoagulant drugs and
their transport in cellular systems is dependent on the efflux pump
P-glycoprotein, encoded by the adenosine triphosphate-binding
cassette (ABCB1) gene (multidrug resistance gene, MDR1) [32,33].
The synonymous 3435T variant (closely linked to 2677T>G and
1236T>C) has been frequently observed among patients requiring
low dose of warfarin [34]. Another study [35] reported that ABCB1
2677GG/3435CC haplotype was associated with lower dose, while the
2677TT/3435TT and 2677GT/3435TT haplotypes were associated with
higher dose of acenocoumarol. These reports suggest that an assessment
of these variants could be useful for predicting P-gp-dependent adverse
drug reactions with oral anticoagulants.

Genome wide association studies have recognized CYP4F2 rs2108622 (Val433Met) as a significant contributor of inter individual
variation in coumarin dose requirement, however its effect size is
smaller than that of CYP2C9 and VKORC1 variants [36,37]. Protein
CYP4F2 catalyzes many reactions involved in drug metabolism. It is
hypothesized that CYP4F2 might interfere in the vitamin K recycling or
could be involved in the metabolism of acenocoumarol as the rs210862
polymorphism is associated with varied levels of FII, FVII, FIX, and FX
after acenocoumarol therapy [37]. There are no studies until now that
have analyzed the rs210862 variant in correlation with anticoagulantinduced
bleeding.

The most common inherited predispositions to thrombophilia
are Factor V Leiden (FVL, coagulation factor V) and the prothrombin
(coagulation factor II) rs1799963 mutation which result in activated
protein C resistance or elevated concentrations of prothrombin (the
immediate precursor of thrombin) respectively [38]. Factor V Leiden
mutation (rs6025) was observed to play a prohemorrhagic role among
patients on anticoagulant therapy in a study by Castori et al. [39].
There is a dearth of further studies analysing the association of FVL in
bleeding with oral anticoagulants.

Patients and Methods

Setting and outcomes

The study was conducted at the Sir Ganga Ram Hospital, a tertiary
health care centre in New Delhi, India. The research protocol was
approved by the Ethics Board Committee of Sir Ganga Ram Hospital
and is in accordance with the ethical standards of Declaration of
Helsinki (World Medical Association). Patients were enrolled
from the outpatient clinic of Department of vascular surgery and
inpatients from the Department of Cardiac surgery. All participants
gave written informed consent. Primary outcome of the present study
was drug-induced bleeding. The ‘case’ definition in the present study
was the patient who develops bleeding during oral anticoagulation
therapy and with oral anticoagulants along with other drugs (drugdrug
interaction). Choice of the anticoagulant for the patients and
management of anticoagulation therapy was carried out by the
respective clinicians. All patients were started on a standard dosing
scheme of acenocoumarol (ACITROM, 2 mg per day from days 1 to
3) or warfarin (WARF, 5 mg per day from days 1 to 4) with a target INR range of 2-3. Doses were adjusted on the basis of the INR of the
patient thereafter. Prothrombin times and International normalized
ratios (INRs) were evaluated once every 1 to 4 weeks depending on the
stability of the INR and anticoagulation level. All study participants
were followed up for approximately 15 months from the time of their
first initiation dose until the end of study period or withdrawal of oral
anticoagulation therapy.

Cohort

Patients ≥ 18 years of age, initiated (first time) on oral anticoagulant
therapy and anticipated long term (>2 years) treatment duration were
eligible. Since the aim of the study was to recognize novel genetic and
non-genetic bleeding predictors, patients with known obvious risk
factors such as abnormal kidney function (e.g., chronic dialysis, renal
transplantation), abnormal liver function (eg, liver cirrhosis), history of
bleeding and malignancy were excluded. Of 1483 patients visiting the
clinic during the study period, only 310 (20.9%) patients fit the eligibility
criteria and were enrolled in the study. The rest of the patients either
lacked a clinical indication for long-term oral anticoagulation therapy
(437, 29.5%), or were previously on anticoagulation therapy (680,
45.9%), or were less than 18 years of age (4, 0.27%), or had comorbid
conditions such as cancer, renal disease or history of bleeding (52, 3.5%).
Of those on acenocoumarol therapy (n=257), 20% of patients (n=51)
were randomly selected to form the validation cohort (CohortVal), while
the rest served as the derivation cohort for acenocoumarol (CohortAC;
n=206) and warfarin (CohortWF; n=53). Warfarin was less commonly
used; hence validation cohort for the same was not available. Four ml of
peripheral blood was collected from the participating patients during
their first clinic visit. For the period of follow-up the patients were
regularly assessed for their INR values, change in dosage, concomitant
use of drugs and bleeding complications. Since it is known that variable
dietary intake of vitamin K can have profound effects on the risk of
bleeding, patients had been therefore counselled regarding the same.
They were advised to maintain a stable intake of vitamin K-containing
foods in their diet. Patient compliance to diet and adherence to therapy
was checked during every follow up.

Assessment of bleeding

Bleeding complications were initially classified as minor (requiring
no additional testing, referral, or outpatient visits), or major (requiring
medical or surgical intervention, major blood loss requiring blood
transfusion of two units or more). For the purpose of developing a
bleeding prediction score, both minor and major bleeding episodes
were pooled to enable the forecast of any type of bleeding among
OAC users. The rationale for this is that although majority of bleeding
is clinically mild, patients with minor bleeds have a significantly
increased relative risk (2.9) of subsequent major bleeding as compared
to those without any minor bleeding [40,41]. Hence, detection of
minor bleeding in addition to major bleeding is clinically crucial as
well. Univariate analyses were performed separately in patients on
acenocoumarol (CohortAC) and warfarin (CohortWF) in addition to the
pooled derivation cohort on both types of anticoagulants (CohortAC+WF).

Selection of candidate SNPs

Twenty one SNPs in seven different genes were selected for analysis.
The method of selection of SNPs in CYP2C9 and VKORC1 is detailed in
supplementary material (Supplemental material 1).

Genotyping methods

CYP2C9, VKORC1, CYP4F2: In addition to the three common variants associated with coumarin response: CYP2C9*2 (rs1799853/
430C>T/ p.Cys144Arg in exon 3), CYP2C9*3(rs1057910/ c.1075A>C/
p.Ileu359Leu in exon 7) and VKORC1-1639G>A (rs9923231/
g.3588G>A in upstream promoter region), the above mentioned
variants were genotyped by resequencing in the remaining patients
as well. CYP4F2 rs2108622 (c.1297G>A/ p.Val433Met in exon 11)
was also genotyped. All of the above SNPs in the CYP2C9, VKORC1 and CYP4F2 genes were analyzed by means of allele-specific PCR or
amplification refractory mutation system (ARMS) PCR using special
primers designed with BatchPrimer3 [42-44] (primers available on
request).

MDR1/ABCB1: The three common polymorphisms in the MDR1/
ABCB1 gene; rs1128503 (c.1236T>C/ p.Gly412Gly in exon 12),
rs2032582 (2677T>G/A/ Ser893Ala/Thr in exon 21) and rs1045642
(3435C>T/ Ile1145Ile in exon 26) that are implicated in variable drug
response were genotyped using previously published methods [45,46].

APOE: The APOE isoforms (e2, e3, e4) that are distinguished by
two non synonymous polymorphisms; rs7212 and rs229358 were
detected using previously published primer sequences [47] for PCR
followed by restriction enzyme HhaI cleavage of the amplified product
to generate allele discriminating DNA fragments.

F5 and F2: Genotyping of factor V Leiden variant (rs6025/
1691G>A) and prothrombin mutation (rs1799963/20210G>A) was
carried out by restriction enzyme digestion of PCR-amplified DNA
based on previously published protocols [48,49] with modifications.

Appropriate quality control was carried out with wild type and
variant genotype control samples. Internal controls were used with
each allele specific primer pair to check for DNA and PCR reaction
quality. The first 20 samples genotyped for each of the above SNPs
were confirmed by resequencing results additionally all genotypes were
confirmed by repeating the test. PCR was carried by standard protocol
using 10 pico moles of each primer.

Statistical analysis

All data analysis was performed with SPSS, version 16.0 (SPSS Inc.,
Chicago, IL). Assuming a bleeding incidence of 10% in individuals
on oral anticoagulants and considering an absolute difference of 10%
(from 5 to 15%) as being worth detecting the sample size needed to be
at least 150 patients, with an a error of 0.05 and a statistical power of
0.9. Two by two contingency chi-squares were used to compare allele
frequencies between groups. The expected genotype frequencies and
the deviation from Hardy-Weinberg equilibrium were analyzed by
Chi square test. The presence of any differences between the groups
with (cases) and without bleeding (controls) was tested by Fisher
exact test for categorical variables and by independent samples t test
for continuous variables. All potential bleeding risk factors identified
from the univariate analyses with a p value <0.05, were included in the
multivariate logistic regression analyses. All comparisons were twotailed.
Variables with p<0.05 in the final model were considered to be
significant contributors and were checked for interaction effects. Two
independent genetic bleeding risk scores (GBRS) were designed to
predict risk of bleeding based on significant factors from CohortAC and
pooled CohortAC+WF. Based on their respective multivariate regression
coefficients (Beta), scores were allotted for each of the bleeding risk
factors in the final model. Scores were awarded to all patients (in
derivation cohort) accordingly. Bleeding risk scores were then stratified
into low and high risk. To measure the discriminative power of the
scoring systems, receiver operating characteristic (ROC) graphs were plotted by taking the predictive probabilities as the test variable. The
c-statistic (area under the curve, AUC) that reflects the concordance
of predicted and observed bleeding episodes was evaluated for both
regression models among different patient subgroups. An AUC <
0.5 meant lack of discriminative power and AUC=1.0 meant perfect
discriminative capacity of the score system.

Validation of GBRS

The GBRS was validated in an independent patient cohort on
acenocoumarol therapy (n=51; CohortVal). Specific scores were
awarded for the presence of risk factors from the model and added
up based on which the patients were designated into the two risk
categories; low and high. A ROC was plotted and the AUC (C statistic)
was calculated to measure the predictive accuracy of the two models
derived from CohortAC and pooled CohortAC+WF. The sensitivity and
specificity of GBRS to predict bleeding accurately was compared
with that of a clinical bleeding risk score (CBRS) score. The CBRS
was derived in a similar method as the GBRS, excluding the genetic
variables. In addition, we report the AUC statistics in a subgroup
analysis of individuals with OAC combined with an antiplatelet drug,
with venous thromboembolism and deep vein thrombosis (DVT) as
clinical indication for OAC.

Results

Characteristics of patient population

The study population had a mean age of 42.51 years (standard
deviation, SD=17.36) and an average BMI of 25.82 (SD=5.8). The
mean follow up period was 475.32 days (SD=172.57) during which
an average of 17.04 (SD=5.31) INR measurements were recorded
for each patient. The study population had 32.98% (SD= 18) of INRs
within the therapeutic range, 13.28% (SD=15.61) INRs >3.0 and
53.73% (SD=20.89) INRs <2.0. Among the 294 patients who stabilized
on either anticoagulant, the average time taken to stabilize was 82.9
days (SD=65.31) and the mean stabilized weekly dose was 20.03 mg
(SD=8.21) and 43.01 mg (SD=16.34) of acenocoumarol and warfarin
respectively.

The demographic, clinical characteristics and clinical parameters of
anticoagulation therapy in the two derivation cohorts (on warfarin and
acenocoumarol) and validation cohort (on acenocoumarol) are detailed
in Supplement Table 1. No significant difference was observed either
between CohortAC and CohortVal or CohortWF and CohortVal. Some
characteristics such as gender, clinical indications, certain concomitant
drugs and follow up time were observed to differ in the patient groups
on acenocoumarol (CohortAC) and warfarin (CohortWF). The genotype
and allele frequency of all SNPs are tabulated (Supplementary Table 2)
and found to fit in Hardy Weinberg equilibrium. The rate of bleeding in
patients was consistent with both anticoagulants (p=0.532). About onethird
(n=26, 30.23%) of bleeding complications occurred in patients
with low INRs (<3.0). During the study period, 68 (21.9%) patients
presented with minor bleeding (mild nasal bleeds, mild bruising
or minor oral bleeds) and 18 (5.8%) with major bleeding (major
dermatologic bleeds, gastrointestinal bleeds or genitourinary bleeds).
The incidence rate was 21.32, 16.86 and 4.46 per 100 person-years for
any type of bleeding, minor bleeding and major bleeding respectively.

Quality of anticoagulation in bleeders and non-bleeders

Variations with respect to quality of anticoagulation therapy among
the cases and controls in each cohort were analyzed. A linear positive
relationship with bleeding was observed with over anticoagulation (INR > 6) and per cent of INRs ≥ 3 and a negative linear association of
bleeding with per cent of INRs ≤ 2 was found to be significant in both
warfarin and acenocoumarol, and the pooled cohort (Supplementary
Table 3). Patients with INR>3.0 were observed to be more likely to
bleed with acenocoumarol. Bleeders on warfarin took a longer time to
stabilize than the non-bleeders. The stabilized weekly acenocoumarol
dose was observed to be lower for bleeders as compared to non-bleeders. Higher numbers of INR measurements were recorded from
bleeders than non-bleeders in the warfarin and pooled cohort.

Derivation and evaluation of bleeding risk indices

Univariate analyses for bleeding complications revealed six
bleeding predictors in the warfarin cohort, five in acenocoumarol
cohort and nine predictors in the pooled cohort (Table 1). The final best
fitting models derived by multivariate regression in cohortAC (GBRSAC) and pooled cohortAC+WF (GBRSAC+WF, CBRSAC+WF) are presented in Table 2. As the multivariate regression coefficient (B) value reflects the
relative weight of each factor in the final model (Table 2), the scores
allocated were based on these values (Table 3). Three important known
risk factors of anticoagulant-induced bleeding (cancer/malignancy,
history of bleeding and hepatic or renal disease) were added in the final
bleeding risk indices based on literature review of bleeding risk factors
in other indices such as HAS-BLED [14], HEMORRHAGES [28],
Shireman et al. [27] and Kuijer et al. [26]. The risk scores for these three
factors were allocated based on the weightage given to them in previous
bleeding risk scores. These factors could not be analyzed in the present
cohort due to the study criteria. Formulation of risk stratification
of score values was done by computing scores for all patients in the
derivation cohorts and plotting ROC graphs to determine their
discriminative powers. With the ascending order of risk (low to
high), a consistent increase in the per cent of bleeders was recorded
with all three prediction algorithms, however the CBRS showed least
percentage of actual bleeding among the patients with high bleeding
risk score (Table 4). The bleeding risk prediction scores resulted in
significant AUCs with both GBRSs (Table 5). As expected, the GBRSAC showed enhanced AUC values in CohortAC while GBRSAC+WF showed
superior AUC plots in its derivation cohortAC+WF as compared to that in
other cohorts. Of the two genetic algorithms, GBRSAC+WF was observed
to be more accurate in its bleeding prediction in patients on warfarin,
patients with DVT, and patients who were on concomitant antiplatelet
therapy. Both GBR scores performed similarly in the cohortAC, patients
with clinical indications other than DVT and patients on OAC without
concomitant antiplatelet therapy.

Variables**

Warfarin (n=53)

Acenocoumarol (n=206)

Pooled (n=259)

% *

Case N=18

Control N=35

P value

Case N=55

Control N=151

P value

Case N=73

Control N=186

P value

CYP2C9 rs1057911

55.6

5.7

<0.001

25.5

16.6

0.149

32.9

14.5

0.001

CYP2C9 rs9332230

50.0

11.4

0.005

32.7

25.8

0.327

37

23.1

0.024

CYP2C9 rs9332172

50.0

22.9

0.045

43.6

32.5

0.138

45.2

30.6

0.027

CYP2C9 rs1057910$$

38.9

17.1

0.081

36.4

23.2

0.058

37

22

0.014

CYP2C9 rs2298037

61.1

45.7

0.288

41.8

55.6

0.079

46.6

53.8

0.298

VKORC1 rs7294

44.4

45.7

0.930

58.2

44.4

0.079

54.8

44.6

0.14

VKORC1 rs55894764

0.0

2.9

0.469

5.5

0.7

0.027

7.7

0

0.255

VKORC1 rs9934438

22.2

14.3

0.469

47.3

28.5

0.011

41.1

25.8

0.016

ABCB1 rs2032582

50.0

62.9

0.368

49.1

64.9

0.04

49.3

64.5

0.025

F5 rs6025

38.9

5.7

0.005

10.9

7.3

0.403

17.8

7

0.009

Proton pump inhibitor

11.1

14.3

1.0

29.1

15.9

0.034

24.7

15.6

0.089

Antiplatelet

77.8

28.6

0.001

70.9

62.3

0.250

72.6

55.9

0.013

Statin$

0.0

28.6

0.011

34.5

21.9

0.064

30.8

15.8

0.253

Arterial occlusive disease

0.0

5.7

0.543

16.4

7.9

0.077

12.3

7.5

0.222

Age>65 years; mean

16.7

2.9

0.108

27.3

7.3

<0.001

24.7

6.5

<0.001

* Unless indicated otherwise
** Only the variables that showed significant correlation in either one or more cohorts are included in this table.
‘Cases’ represent the patients who experienced one or more bleeding episodes while ‘controls’ refer to patients who did not bleed during the long term follow up.
$ Protective effect, negative correlation with bleeding. All factors that showed a significance level of p<0.10 are tabulated above, although only the ones with p<0.05 (in bold
font) were included in the logistic multivariate regression model.
$$An independent correlation analysis of CYP2C9 rs1799853 and rs1057910 variants among the patients with major bleeding events (n=18) revealed a significant
association of rs1057910 variant allele with major bleeding (p=0.001).

Table 1: Bleeding predictors in univariate analysis.

Bleeding predictor variables

GBRSAC

GBRSAC+WF

CBRSAC+WF

B

Exp B (95% CI)

B

Exp B (95% CI)

B

Exp B (95% CI)

Factor V Leiden

1.12

3.08 (1.26-7.51)

Age >65 years

1.42

4.14 (1.71-10.06)

1.60

4.95 (2.13-11.48)

1.5

4.46 (2.0-9.93)

VKORC1 rs9934438

0.82

2.27 (1.28-4.03)

0.74

2.09 (1.25-3.51)

VKORC1 rs55894764

2.47

11.86 (1.13-124.77)

CYP2C9 rs1057911

1.13

3.08 (1.56-6.1)

Antiplatelet

0.67

1.95 (1.06-3.57)

GBRS - genetic bleeding risk score, CBRS - clinical bleeding risk score
Beta (B) is the multivariate coefficient value that reflects the relative weight of each factor in the final model. Exponential (Exp) Beta is the odds ratio or likelihood ratio of
bleeding for each predictor in the model. Colinearity was checked and did not exist.

Table 2: Best fitting model in two cohorts using forward stepwise method of multivariate logistic regression.

Predictor variables

Scores assigned

GBRSAC

GBRSAC+WF

CBRSAC+WF

Factor V Leiden

-

1

-

Age >65 years

1

2

1

VKORC1 rs9934438

1

1

-

VKORC1 rs55894764

2

-

-

CYP2C9 rs1057911

-

1

-

Antiplatelet

-

-

1

Cancer / malignancy$

1

1

1

History of bleeding$

1

1

1

Hepatic or renal disease$

1

1

1

$Variables added in bleeding risk indices based on literature review of known bleeding risk factors in other indices such as HAS-BLED [14], HEMORR2HAGES [28],
Shireman et al [27] and Kuijer et al [26]. However, these already established risk factors were not analyzed in the current population due to the study criteria.

Table 3: Scores assigned to the bleeding predictor variables in the three bleeding risk indices.

Predicted risk

GBRSAC

GBRSAC+WF

CBRSAC+WF

Score

N

Actual Bled (%)

Score

N

Actual Bled (%)

Score

N

Actual Bled (%)

Low

0 - 1

189

22.2

0 - 2

240

24.2

0 - 1

237

24.5

High

≥ 2

17

76.5

≥3

19

78.9

2

22

68.2

The GBRSAC was derived from the patients on acenocoumarol (N=206); while GBRSAC+WF and CBRSAC+WF were derived from the pooled cohort (N=259).

GBRS - genetic bleeding risk score; Area under the curve (AUC) denotes the proportion of cases that were accurately distinguished into the correct risk categories of
bleeding. The AUC in bold font showed significantly higher values with GBRSAC+WF. OAC (Oral anticoagulant)

Table 5: A comparison of the discriminative power of the two logistic equations calculated as the area under curve (AUC) in different groups within the cohort.

Validation

In the validation cohort, 13 (25.5%) patients experienced bleeding
(Supplementary Table 2). The high and low risk groups determined
by the three prediction algorithms in the validation cohort (Table
6) revealed that GBRSAC+WF demonstrated the lowest baseline risk of
bleeding among the low risk group (17.8%) and correctly identified
highest proportion of bleeders (83.3%) among the high risk groups.
The GBRSAC+WF proved to be the best prediction algorithm for bleeding
as it had the highest sensitivity (38.5%), positive predictive value (83.3),
negative predictive rate (NPR; 82.2%) with lowest false positive rate
(FPR; 16.7%) and false negative rate (FNR; 17.8%) as compared to the other two indices. The clinical bleeding risk score (CBRS) performed
poorly with the lowest sensitivity (15.4%) and PPV (40.0%) with
highest FPR (60%). Due to the limited number of clinical factors in
the CBRS, it’s AUC (0.611; CI 0.438-0.585) was much lower than those
observed with genetic algorithms; GBRSAC (0.719; CI 0.544-0.894) and
GBRSAC+WF (0.757; CI 0.600-0.914).

Discussion

The study adds new knowledge with regards to important genetic
and non-genetic predictors of bleeding risk. Integration of these
bleeding predictors in the routine anticoagulation management could
help caution the clinician against prescription of high doses in the high
risk patients. The high risk patient group may also benefit from close
and frequent monitoring of INR, lower intensity of anticoagulation
(low target INR) or an alternate new oral anticoagulant. These
measures can effectively reduce the number of bleeding episodes,
thus sparing the patients from adverse outcomes and reducing the
economic burden of hospital admissions due to anticoagulant related
ADRs. The present study is the first to devise and validate a genetic
scoring scheme for predicting bleeding among first time users of oral
anticoagulants. The GBRS uses only some variables that are easily
obtained from new patients (age, history of malignancy/cancer,
bleeding, hepatic or renal disease), along with two or three genetic
markers (GBRSAC and GBRSAC+WF respectively). The addition of genetic
variables was observed to increase the prediction sensitivity by twofolds
as compared to use of clinical and demographic variables alone
(Table 6). Although, the sample size of warfarin users was small
(n=53) and a separate algorithm could not be derived, the GBRSAC+WF derived from the pooled CohortAC+WF proved to effectively distinguish
bleeders from non-bleeders among warfarin users (Table 5). The
GBRSAC+WF was observed to be the best scoring scheme by all statistical
measures (Table 6) as well as the preferred score for use in patients
on either types of coumarin derivatives, patients with DVT as clinical
indication and patients on concomitant antiplatelet therapy (Table
5). The GBRSAC+WF also showed lower FNR and higher PPV when
validated in an independent cohort on acenocoumarol therapy (Table
6) as compared to that observed in the derivation cohort (Table 4).
The overall better performance of the GBRSAC+WF over GBRSAC may
be due to the higher frequency of CYP2C9 rs1057911 variant allele
(0.105) in the study population as compared to VKORC1 rs55894764 variant allele (0.010) (Table 2) included in the GBRSAC+WF and GBRSAC respectively. Although the sensitivities of both GBRSs are modest, the
high specificity (Table 6) suggests that the scores are able to correctly
identify patients who are not likely to bleed. This low sensitivity could
be due to the unequal proportions of cases and controls in the study
(27.7% cases and rest served as controls).

Table 6: Validation of the bleeding prediction models in an independent cohort on acenocoumarol therapy.

Older age (>60/>65 years) has been consistently associated with
increased bleeding risk in literature and therefore included in most
bleeding risk stratification schemes [13,26-28] as well as the present
scoring scheme. Concomitant use of antiplatelet drugs, that was
identified as a significant risk factor for bleeding (in univariate analyses)
in the present study has also been identified frequently in previous
studies [27,50,51]. There are mixed reports of the role of comorbidities
in anticoagulation-related bleeding. Although hypertension, diabetes
[51], and stroke [13,14] have been included in some other bleeding
risk scores, a large systematic literature review of 41 studies that
evaluated the association of diabetes, hypertension, older age, chronic
heart failure and cerebrovascular disease with anticoagulation related
bleeding [52] observed low strength of evidence for all these factors
except older age which showed moderate strength of association. A
similar trend is reflected in the present study as well. Our findings did
not show increased bleeding in females as has been reported previously
[26,27,51]. Few other factors that have been correlated with bleeding
in literature, such as history of stroke [13,14] and smoking [51] failed
to replicate a similar relationship in our cohort. The higher number of
INR measurements and longer time taken to stabilize among bleeders
implies that those who bled required more frequent INR monitoring
(possibly due to fluctuating INRs) as compared to controls. This is
parallel to the direct correlation of the proportion of out-of-range INRs
(>3.0, <2.0) and the risk of bleeding. Although ‘labile INRs’ (therapeutic
time in range <60%) is one of the bleeding predictors in the HAS-BLED
score index [14], we did not analyse it in the present study as the GBR
score was primarily aimed for coumarin-naive patients.

CYP2C9 variants have been previously associated with ‘major’
or ‘severe and life threatening’ bleeding events (but not with minor
bleeding) in patients on acenocoumarol [53] and warfarin [10,12,54].
The present study showed no significant increase in the likelihood of
bleeding among the *2 or *3 carriers, akin to a few other studies with
warfarin [34,55]. This may be due to inclusion of all types of bleeding
(minor and major) in the derivation of GBRS. Also, the frequency of
CYP2C9 *2 and *3 variants in the Asian-Indian population is reported
to vary from other ethnic populations [56]. However, a significant
association of *3 allele was observed specifically with major bleeding
(n=18). But, due to the small numbers of major bleeding events, the
validity of this association is questionable in the present population.
A synonymous variant (rs1057911) reported to be in strong linkage
disequilibrium with *3 [57], and also classified as a tag SNP to
identify CYP2C9*18 haplotype (rs1057910, rs1057911, rs72558193)
was observed to show highly significant marker for bleeding in the present study. This polymorphism was reported to be significantly
associated with warfarin dose variance, explaining 14.5% of variance
[58]. Although the exact mechanism of the role of this polymorphism
in bleeding is unknown, synonymous changes are being increasingly
reported and are known to affect the translation efficiency by interfering
with both mRNA stability and the protein translation rate [59,60]. Two
intronic CYP2C9 polymorphisms- rs9332230 and rs9332172 showed
high prevalence in the cases as compared to controls. There are no
previous reports of similar association. The mechanism of this effect
is unknown, however it is likely that these variants could be linked to
other non-synonymous SNPs (such as CYP2C9 *3 and *2) or modify
the mRNA expression, transcription or regulation of the CYP2C9 enzyme that may result in slower drug metabolism and enhanced
pharmacologic activity leading to toxicity (bleeding). Further in vitro functional and pharmacokinetic studies are warranted to confirm their
role in anticoagulant induced bleeding.

Polymorphisms in VKORC1; rs9934438 (1173C>T) and
rs55894764 were significantly associated with acenocoumarol-induced
bleeding but not with warfarin. However, 1173C>T withstood
multivariate regression analyses and showed significant predictability
in both genetic algorithms (GBRSAC, GBRSAC+WF). Despite the small
sample size of warfarin users in the current study, the absence of
correlation of VKORC1 variants with warfarin-induced bleeding has
been reported previously as well [12]. A high haemorrhagic risk (but
not statistically significant) has been reported with 1173C/T variant in
warfarin-users [12]. Another study [11] observed increased bleeding in
Phenprocoumon users who carried the 1173T allele. Thus, the present
study is the first report of significant association of VKORC1 1173C>T
with acenocoumarol-induced bleeding.

Factor V Leiden mutation, a known genetic risk factor for
thrombophilia [38] was unexpectedly observed to increase bleeding
risk among patients on anticoagulant therapy in the current study.
Similar observation was made by Castori et al. [39]. Although, the exact
mechanism by which FVL could cause bleeding is not known, some
previous observations seem to support its prohemorrhagic role. A high
frequency of the FVL mutation was reported in cases of hemorrhagerelated
preterm delivery [61] and intraventricular hemorrhage in
preterm infants [62,63]. The relatively hypercoaguable state in normal
pregnancy and the protein C and S deficiency among the preterm infants
is analogous to the state of anticoagulated patients taking coumarin
derivatives. It is hypothesized that FVL thrombophilic mutation may
aggravate this hemostasis shift and heighten the risk of clots in such
patients and may someway facilitate the rupture of delicate blood
vessels, resulting in hemorrhage [61]. Another (less likely) explanation
could be linkage disequilibrium of the FVL mutation with unknown
genetic variants that can alter the bleeding propensity while on oral
anticoagulants [62-64]. Previous studies have shown that about 9.7%
of FVL carriers may also have a combination of two or more variant CYP2C9/VKORC1 alleles [65], rendering them at very high risk of
anticoagulation-induced bleeding.

The ABCB1 2677 TT and GT haplotypes previously associated with
higher acenocoumarol dose [35] was observed to have a protective
effect against bleeding complications in the current study. Although
this is the first report documenting the protective effect of ABCB1 2677
variant, a previous study [34] documents lesser prevalence of ABCB1
variants among patients with warfarin-induced bleeding.

Pharmacokinetic drug interactions leading to bleeding with
warfarin or acenocoumarol could occur with drugs that inhibit CYP2C9 or CYP2C19, affecting the drug concentrations, and subsequently
enhancing its pharmacologic effect. From the pharmacodynamics
perspective, drug interactions could also occur with drugs that interfere
with platelet aggregation or synthesis of clotting factors, resulting in
a synergistic effect. In the present study, all cases where concomitant
drugs were observed to increase the likelihood of bleeding, respective
patient records were analysed comprehensively for confirming
the hemorrhagic role of drug-drug interactions. Use of Naranjo’s
probability scale [66] indicated ‘probable’ (scores 5-8) or ‘possible’ (1-
4) relationships of the respective drug in combination with coumarin
in all suspected cases of drug-drug-interaction.

Co-administration of proton pump inhibitors (PPIs, Pantoprazole
and Rabeprazole) has occasionally been associated with potentiation
of acenocoumarol [67,68] and less commonly with warfarin [69-71].
However, until now bleeding was not analyzed as an outcome with
co-administration of PPI in OAC users. The current study suggests a
moderately significant role of interaction of PPIs with acenocoumarol
but not with warfarin. As PPIs are essentially metabolized by CYP2C19
(and sometimes CYP2C9 and CYP3A4) which is partially involved
in the metabolic clearance of the potent R-acenocoumarol [72],
the resulting inhibition of CYP2C19 may reduce the clearance of
acenocoumarol [73], leading to toxicity as observed in current study.
On the other hand, the potent S-enantiomer of warfarin is metabolised
by CYP2C9, which is inhibited to a lesser degree than CYP2C19.

Concomitant use of statin with OAC is reported to increase the
anticoagulant response ensuing elevated INRs, dose reductions (10%-
27%) and/or toxicity [74-76]. This is known to be caused by inhibition
of CYP3A4 enzyme by statin and displacement of coumarin drug from
protein binding site. On the other hand, a large study on warfarin users
with atrial fibrillation has reported that long-term statin use (>1 year)
may be associated with a decreased risk of bleeding [77]. Our findings
reiterate the protective effect of statin against bleeding complications
among patients on long term warfarin use. However, this significant
effect was not observed with acenocoumarol. It is likely that different
generic drugs vary the role of statin interaction in bleeding with
anticoagulants. Nonetheless, the small sample size of warfarin cohort
limits the validity of the finding and will need to be replicated in a larger
cohort on warfarin therapy before any conclusions or extrapolations
can be drawn from the finding.

Limitations

a. The present study is limited in not analysing some already
established bleeding risk factors such as alcohol abuse [14,27,28]
due to insufficient data. Also, history of previous bleeding
[13,14,27,28], liver and renal disease [14,28] were not analyzed
in the present cohort as they were a part of the exclusion
criteria to enable us to focus the study on exploring the genetic predictors of bleeding. Exclusion of the above recognized
non-genetic risk factors from the study may make it difficult
to judge the true relative effect of genetic factors in a clinical
population. Therefore, we present a more extensive bleeding risk
stratification scheme that incorporates the above factors along
with the novel genetic risk factors reported in the present study
(Table 3). Three important known risk factors of anticoagulantinduced
bleeding (cancer/malignancy, history of bleeding and
hepatic or renal disease) were added in the final bleeding risk
indices based on literature review of bleeding risk factors in
other indices. Although these additional factors could not be
analyzed in the current population, several studies have reported
their important contribution to anticoagulant-induced bleeding
in patients. Thus, this enhances the predictive power and scope
of the current GBRS.

b. The study assumed a drug class effect for statins, antiplatelets and
PPIs because of the exploratory nature of our study and the small
frequency of patients on each type of generic drug. Preparationspecific
protective or adverse effects could occur. For example,
literary evidence of interaction of different types of statins with
warfarin indicates that the warfarin interaction potential is more
pronounced for simvastatin [74-76] than for atorvastatin or
other statins [78]. Statins were observed to be one of the most
commonly used drugs in the current study (n=62, 23.9%),
however due to the limited number of patients on therapy with
different generic forms of statin, we could not analyze their
independent effect on bleeding outcome. Therefore, we cannot
exclude the possibility that the effects we observed with statins
may differ according to individual statin preparations.

c. Small sample size of patients on warfarin as compared to
acenocoumarol limits the strength of bleeding risk factors
that were observed exclusively in the cohortWF. Larger studies
analyzing bleeding complications with warfarin will be required
to confirm some of the current findings.

d. Some dissimilarity in bleeding predictors was observed with
the type of OAC i.e. warfarin and acenocoumarol (Table 1).
Additionally, the quality of anticoagulation with the long-acting
warfarin and the rapid-acting acenocoumarol differed in some
aspects. The mean proportion of INR within therapeutic range
was significantly greater among patients on warfarin than those
on acenocoumarol (Supplemental Table 4). Occurrence of
bleeding events appears to be higher in warfarin users but the
difference was not statistically significant. This may be due to
the longer mean follow up in the warfarin cohort. Also, the per
cent of non-therapeutic INR (<2.0) was lower with warfarin than
acenocoumarol with borderline significance. A comparative
study of quality and hemorrhagic risk with warfarin and
acenocoumarol revealed that patients treated with acenocoumarol
had a higher risk of presenting with an INR ≥ 6, however no
statistically significant differences were reported in therapeutic
stability [79]. At present we have no clear explanation for risk
differences between the two coumarin anticoagulants. More
likely, the difference in bleeding predictors may be explained by
the diverse pharmacokinetics of acenocoumarol and warfarin.
The two coumarin derivatives have variable maintenance dose
(lower for acenocoumarol), plasma concentration (lower with
acenocoumarol), plasma clearance (faster with acenocoumarol),
terminal elimination half life (shorter with acenocoumarol)
and elimination kinetics (biphasic for acenocoumarol) [80]. Most importantly, the pharmacogenetic variability among the
coumarins is likely to cause differential protein-drug binding
and different drug-drug interactions that may in turn attribute to
variation in genetic bleeding predictors with the two coumarin
anticoagulants.

Conclusions

Genetically determined pharmacokinetic and pharmacodynamic
capacity in an individual can dramatically alter the toxin and metabolite
levels from those normally expected, which is crucial for drugs with a
narrow therapeutic index, like acenocoumarol and warfarin. Genetic
screening for bleeding predictors using simple scoring method have the
potential to remove some of the scientific uncertainties in toxicity cases
and can greatly reduce the economic burden of adverse drug reactions.
However, the cost versus benefit of introducing such a form of genetic
prediction will need to be further studied depending on the population
incidence of bleeding and the cost of the rapid genetic test. It has been
reported that a 6.9% improvement in the time spent within therapeutic
range significantly reduced major hemorrhage by one event per 100
patient-years of treatment [81]. Hence, predictive bleeding scoring
index along with improvement in the quality of anticoagulation by
careful INR monitoring, proper management guidelines and patient
education regarding concomitant drugs, vitamin K diet and signs of
bleeding can decrease the incidence of bleeding complications.

Executive Summary

• The incidence rate was 21.32, 16.86 and 4.46 per 100 personyears
for any type of bleeding, minor bleeding and major
bleeding respectively.

• Genetic Bleeding Risk Score (GBRS)AC+WF identified 78.9%
of bleeders as the ‘high risk’ group and demonstrated an area
under the curve (AUC) of 0.855 in patients on warfarin, 0.706 in
patients on either oral anticoagulant and 0.802 in patients with
deep vein thrombosis. GBRSAC+WF had a specificity of 97.4%, false
positive rate of 16.7% and false negative rate of 17.8%.

• The GBRS was validated to perform better than the Clinical (nongenetic)
Bleeding Risk Score (CBRS). The sensitivity increased
two-folds with GBRSAC+WF as compared to CBRS.

Conclusions

• Genetic screening for bleeding risk using the current simple
scoring method has the potential to remove some of the scientific
uncertainties in toxicity cases.

• Predictive bleeding score along with improvement in the quality of
anticoagulation by careful INR monitoring, proper management
guidelines and patient education regarding concomitant drugs
and signs of bleeding can decrease the incidence of bleeding
events. This can greatly reduce the economic burden of adverse
drug reactions.

Financial Disclosure/Conflict of interest

Ishwar C Verma, Renu Saxena and Risha Nahar had received research grant for the above work from the Sir Ganga Ram Hospital. For the remaining authors no
conflicts of interest are declared.

Acknowledgements

The authors are thankful to the Sir Ganga Ram Hospital for funding. The
funding source was not involved in the study design, analysis and interpretation of
data or in the writing of the manuscript.